Run:AI is bridging the gap between data science and computing infrastructure by creating a high-performance compute virtualization layer for deep learning, speeding the training of neural network models and enabling the development of large AI models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned for full utilization of expensive GPU compute.

Job Description

Run:AI provides organizations with a world-class machine learning platform to improve productivity and efficiency for data scientists. Our product provides a Run:AI unique HPC scheduler, relies on Run:AI advanced GPUs virtualization technology and makes GPUs first class citizens in Kubernetes.

In this role you will be responsible for the following:

• Work with Run:ai customers on a daily basis to guide and assist with their onboarding process for run:ai
• Installation of Run:AI software, Including Kubernetes, NVIDIA Drivers, Docker on cloud and air-gapped environments.
• Work with various type of Kubernetes orchestration tools and solve complex issues in container environment
• Perform training for customers Researchers and Administrators
• Creation and sharing of best practices, technical content (e.g. how to docs, tips and tricks)
• Work closely with Product and Engineering teams to define Feature Requests and customers solve issues and bugs in an efficient manner

Qualifications

• 5+ year of Experience in a customer facing role (preferably as a PS engineer)
• BS in Engineering, Computer Engineering, Computer Science or equivalent experience
• Experience presenting to technical audiences and creating technical content for developers

• Hands on experience working with Kubernetes based tools
• Experience with technologies like Docker, Containers and Kubernetes
• Strong analytical and problem-solving skills
• Excellent written and verbal communication skills

• Knowledge of deep learning, models and frameworks such as TensorFlow, PyTorch - Advantage
• Familiarity with the MLOPS and ML pipeline tools - advantage